Preparation of novel high copper ions removal membranes by embedding organosilane-functionalized multi-walled carbon nanotube

Zeng, Guangyong; Yu, Zongxue; Yang, Xi; Yang, Ruizhi; Zhang, Lei

doi:10.1002/jctb.4820

Cited by 58 publications

(23 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Swati et al (2019) used a block-wise fine-tuning algorithm based on transfer learning to fine-tune pretrained CNN on an MRI brain tumor dataset and obtained average accuracy of 94.82% under five-fold cross validation. Rehman et al (2019) employed three pretrained CNNs (AlexNet (Krizhevsky et al, 2017), GoogLeNet (Zeng et al, 2016), and VGGNet (Simonyan and Zisserman 2015)) to classify brain tumor MRI images with two different transfer learning techniques (fine-tune and freeze), and the fine-tuned VGG16 architecture showed the highest accuracy of 98.69%. Smailagic et al (2018) sampled the instances which had the longest distance from other training samples in a learned feature space.…”

Section: Transfer Learning and Active Learning For Medical Imagingmentioning

confidence: 99%

A Transfer Learning–Based Active Learning Framework for Brain Tumor Classification

Hao

Namdar

Liu

et al. 2021

Front. Artif. Intell.

View full text Add to dashboard Cite

Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) at an early stage plays a key role in successful prognosis and treatment planning. With recent advances in deep learning, artificial intelligence–enabled brain tumor grading systems can assist radiologists in the interpretation of medical images within seconds. The performance of deep learning techniques is, however, highly depended on the size of the annotated dataset. It is extremely challenging to label a large quantity of medical images, given the complexity and volume of medical data. In this work, we propose a novel transfer learning–based active learning framework to reduce the annotation cost while maintaining stability and robustness of the model performance for brain tumor classification. In this retrospective research, we employed a 2D slice–based approach to train and fine-tune our model on the magnetic resonance imaging (MRI) training dataset of 203 patients and a validation dataset of 66 patients which was used as the baseline. With our proposed method, the model achieved area under receiver operating characteristic (ROC) curve (AUC) of 82.89% on a separate test dataset of 66 patients, which was 2.92% higher than the baseline AUC while saving at least 40% of labeling cost. In order to further examine the robustness of our method, we created a balanced dataset, which underwent the same procedure. The model achieved AUC of 82% compared with AUC of 78.48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.

show abstract

Section: Transfer Learning and Active Learning For Medical Imagingmentioning

confidence: 99%

A Transfer Learning–Based Active Learning Framework for Brain Tumor Classification

Hao

Namdar

Liu

et al. 2021

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…The most notable architectures of the CNN technique are ResNet [33], VGGNet [34], GoogLeNet [35], AlexNet [36], and DenseNet201 [37]. Considering the safety-critical nature of the healthcare sector, applications used in healthcare must have high reliability.…”

Section: A Convolutional Neural Network (Cnns)mentioning

confidence: 99%

A Selective Mitigation Technique of Soft Errors for DNN Models Used in Healthcare Applications: DenseNet201 Case Study

2021

View full text Add to dashboard Cite

Deep neural networks (DNNs) have been successfully deployed in widespread domains, including healthcare applications. DenseNet201 is a new DNN architecture used in healthcare systems (i.e., presence detection of the surgical tool). Specialized accelerators such as GPUs have been used to speed up the execution of DNNs. Nevertheless, GPUs are prone to transient effects and other reliability threats, which can impact DNN models' reliability. Safety-critical systems, such as healthcare applications, must be highly reliable because minor errors might lead to severe injury or death. In this paper, we propose a selective mitigation technique that relies on in-depth analysis. First, we inject the DenseNet201 model implemented on a GPU via NVIDIA's SASSIFI fault injector. Second, we perform a comprehensive analysis from the perspective of kernel and layer to identify the most vulnerable portions of the injected model. Finally, we validate our technique by applying it to the top-vulnerable kernels to selectively protect the only sensitive portions of the model to avoid unnecessary overheads. Our experiments demonstrate that our mitigation technique achieves a significant reduction in the percentage of errors that cause malfunction (errors that lead to misclassification) from 6.463% to 0.21%. Moreover, the performance overhead (the execution time) of our technique is compared with the well-known protection techniques: Algorithm-Based Fault Tolerance (ABFT), Double Modular Redundancy (DMR), and Triple Modular Redundancy (TMR). The proposed solution shows only 0.3035% overhead compared to these techniques while correcting up 84.8% of the SDC errors in DenseNet201, remarkably improving the healthcare domain's model reliability.

show abstract

“…Amine group is one of the most hydrophilic groups that have robust reactivity with cations or other electrophilic compounds. [107,108] Numerous reactive sites available on the surface of functionalized CNT provided robust interaction between the two components, which lead to the uniform dispersion and heavy loading of metal oxides. Figure 4(a) shows the detailed experimental procedure used to synthesize the NiMoO 4 -CNT nanocomposite.…”

Section: Modification Of Carbon For Better C-tmo Interaction and Tmo mentioning

confidence: 99%

Carbon Transition‐metal Oxide Electrodes: Understanding the Role of Surface Engineering for High Energy Density Supercapacitors

Tomboc

Gadisa

Jun

et al. 2020

Chemistry An Asian Journal

View full text Add to dashboard Cite

Supercapacitors store electrical energy by ion adsorption at the interface of the electrode-electrolyte (electric double layer capacitance, EDLC) or through faradaic process involving direct transfer of electrons via oxidation/ reduction reactions at one electrode to the other (pseudocapacitance). The present minireview describes the recent developments and progress of carbon-transition metal oxides (C-TMO) hybrid materials that show great promise as an efficient electrode towards supercapacitors among various material types. The review describes the synthetic methods and electrode preparation techniques along with the changes in the physical and chemical properties of each component in the hybrid materials. The critical factors in deriving both EDLC and pseudocapacitance storage mechanisms are also identified in the hope of pointing to the successful hybrid design principles. For example, a robust carbon-metal oxide interaction was identified as most important in facilitating the charge transfer process and activating high energy storage mechanism, and thus methodologies to establish a strong carbon-metal oxide contact are discussed. Finally, this article concludes with suggestions for the future development of the fabrication of high-performance C-TMO hybrid supercapacitor electrodes.[a] Dr.

show abstract

Preparation of novel high copper ions removal membranes by embedding organosilane-functionalized multi-walled carbon nanotube

Cited by 58 publications

References 43 publications

A Transfer Learning–Based Active Learning Framework for Brain Tumor Classification

A Transfer Learning–Based Active Learning Framework for Brain Tumor Classification

A Selective Mitigation Technique of Soft Errors for DNN Models Used in Healthcare Applications: DenseNet201 Case Study

Carbon Transition‐metal Oxide Electrodes: Understanding the Role of Surface Engineering for High Energy Density Supercapacitors

Contact Info

Product

Resources

About